The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed in the form of newer machines such as Medical Resonance Imaging (MRI) scanners, Computed Axial Tomography (CAT) scanners, etc. Because of large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
Digital medical images are constructed using raw image data obtained from a scanner, for example, a CAT scanner, MRI, etc. Digital medical images are typically either a two-dimensional (“2-D”) image made of pixel elements or a three-dimensional (“3-D”) image made of volume elements (“voxels”). Such 2-D or 3-D images are processed using medical image recognition techniques to determine the presence of anatomical structures such as cysts, tumors, polyps, etc. Given the amount of image data generated by any given image scan, it is preferable that an automatic technique should point out anatomical features in the selected regions of an image to a doctor for further diagnosis of any disease or condition.
Automatic image processing and recognition of structures within a medical image is generally referred to as Computer-Aided Detection (CAD). A CAD system can process medical images and identify anatomical structures including possible abnormalities for further review. Such possible abnormalities are often called candidates and are considered to be generated by the CAD system based upon the medical images.
One process often involved in CAD systems is the segmentation of medical images. Image segmentation is the process of partitioning an image into multiple segments. Image segmentation is typically used to locate objects of interest (e.g., abnormalities such as lesions) as candidates for further review.
One type of image segmentation technology is region-based, which is also classified as a pixel-based image segmentation since it involves the selection of initial seed points. Region growing is the simplest region-based segmentation that groups pixels or sub-regions into larger regions based on a pre-defined criteria. The pixel aggregation starts with an initial set of “seed” points, and regions are then grown from these seed points to adjacent points that have similar properties (e.g., gray level, texture, color, shape, etc.).
Pixel-based segmentation (e.g., region growing) is fast, conceptually simple, and better than, for example, edge-based techniques in noisy images where edges are difficult to detect. However, pixel-based segmentation methods do not have global shape information when processing each pixel locally. Therefore, at each iteration, the segmentation process only makes a decision whether the pixel in question should be included in the segmentation mask and processes that pixel's neighboring pixels recursively. The segmentation results are prone to “leaks” or “bleed-through” artifacts in which the segmentation mask floods outside the object of interest and the boundary between objects are blurry or not clearly distinguishable. This can cause the segmentation method to, for example, erroneously categorize healthy tissue as part of an abnormality (e.g., lesion).
Therefore, there is a need for improved systems and methods for pixel-based segmentation algorithms to prevent segmentation leakage.